In recent years, the multimedia technology has matured a lot and the video devices are widely used everywhere, such as monitoring and military affairs, etc. Furthermore, the requirements for video quality are also increasing. However, the video quality is poor due to some inevitable objective factors, and thus fails to meet the demand. Hence, the research on how to enhance and denoise the video images in real time is very significant.
The poor quality of the video images is caused by the following two aspects: (1) Video capturing systems generally can capture images with better quality in a fine day. However, in a bad weather with thick fog or dust, etc., or during the night without enough light, the contrast of images captured by the system is always low, and nothing of value can be obtained from the images. With the video image enhancing technology, the visual effect of the video images can be effectively improved to highlight the interesting information and discard the useless information. (2) The poor quality of images and deviation from the real situation are caused by the noises in the process of image capturing and transmission, greatly decreasing the accuracy of the extracted information. Therefore, it is essential to eliminate the noises before utilizing the video images, to improve the denoised images and highlight the video image characters.
The gray image enhancing methods are more mature, including contrast enhancing method, histogram equalization method, homomorphic filtering method, wavelet transforming method, etc. The color image enhancing method, such as the retines algorithm based on the path comparison, is mainly based on retinex theory, which is good for dynamic range compression and color constancy. However, the algorithm has a complex calculation, high dependency on the geometric paths and sample noises, and is also ineffective for the images with noises. Therefore, the algorithm based on the retinex theory needs to be improved.